digital elevation model generation by robotic total station instrument

DIGITAL ELEVATION MODEL GENERATION BY ROBOTICTOTAL STATION INSTRUMENT

Digital elevation models (DEMs) are indispensabletools in many environmental and natural resourceapplications. This term characterizes a modelingtechnique rather than the data that are described

by an elevation model. DEMs are frequently derived fromcontour lines. The accuracy of such DEMs depends on differ-ent factors.

A DEM is a numerical representation of topography, usuallymade up of equal-sized grid cells, each with an elevationvalue. Its simple data structure and widespread availabilityhave made it a popular tool for land characterization.1

The widespread availability of computing facilities and sour-ces of DEMs enhance their utilization in many environmentaland natural resource applications. The list of applications isprogressively growing: visibility analysis, erosion modeling,surface hydrology, watershed modeling, geomorphology, landsliding, remote sensing applications, agriculture, and ecosys-tem modeling are some examples.2–4

Errors in any DEM might adversely affect the ability to repre-sent the terrain and hence affect its usefulness for a particularapplication.5 Research indicates that the quality of any DEM isaffected by many factors. First, the increase in sampling inter-vals of elevations causes decreased levels of reliability in themapped topographic variables6 and therefore decreases theaccuracy of the DEMs.7 Second, the interpolation algorithm isused to generate the DEM, where results may differ signifi-cantly when different algorithms, such as inverse distanceweighting, local and global polynomial trend surfaces, kriging,or specialized algorithms such as ANUDEM, are used.8–11

Third, the chosen grid cell size (resolution) and the nature ofthe terrain. Gentle terrain was more accurately representedthan complex terrain using the same DEMs. The discrepancyin the accuracy became even larger at a coarse resolution.12

High-resolution DEMs are required to represent fine varia-tions in topography, especially in complex terrain,7,13 whichincreases the volume of the data to be stored and mightinvolve some redundancy. Furthermore, too small a grid cellsize may result in estimates that are much more detailed thanis relevant for the process being modeled. The solution is toselect a resolution that is as coarse as possible while stillmeeting a defined accuracy to serve the specific purpose. Lim-ited research has been carried out to assess the impact of theinaccuracy of the source data, processing errors, and the DEMresolution on the accuracy of the DEM and its derivatives.6

DEMs are generated by different sources such as aerial photos,satellite images, contour maps, and field data. The most accu-

rate DEM is generated by field data. As mentioned before, theonly error source in field data is the measurement error.

It is required that points are known coordinates x, y, z togenerate DEM by field data. Point number and distributionare important in the generating of a DEM. But, measurementof more points in the field increases the cost and the time.

Creation of a three-dimensional (3-D) network structure ofearth surface is among application of DEM, created from dig-ital elevation data by triangulated irregular network (TIN),vertical/horizontal lines at regular intervals, isoelevationcurves, and profiles. Triangulated irregular network has adja-cent triangular faces matching elevations sampled at irregu-lar intervals, thus providing data distribution at variableintervals.14 The TIN model represents a surface as a set ofcontiguous, nonoverlapping triangles. The triangles are madefrom a set of points called mass points. Mass points can occurat any location; the more carefully selected, the more accuratethe model of the surface

Advantages of TIN are the ability to describe the surface atdifferent levels of resolution and efficiency in storing data.Disadvantages of TIN are that in many cases they requirevisual inspection and manual control of the network.15

Surfer 8 software (Golden Software Inc., Golden, CO) is themost powerful, flexible, and easy-to-use contouring and 3-Dsurface mapping package available. Surfer 8 software easilyand accurately transforms XYZ data into spectacularly color-ful contour, surface, wire frame, shaded relief, image, post,and vector maps in minutes. This software quickly interpo-lates irregularly or regularly spaced data into a regularlyspaced grid and creates grids from up to 1 billion XYZ datapoints.16

In this study, a robotic total station geodetic measurementinstrument, which could measure data in regular intervals,was used. Digital elevation model generating efficiency of thisinstrument was investigated.

MATERIALS AND METHODS

The TOPCON 8203M robotic total station (Topcon PositioningSystems, Inc., Livermore, CA) has been used to obtain pointsthat are at intervals of 20, 40, 80, and 100 cm in vertical andhorizontal directions. Surfer 8.0 software has been used togenerate DEM surfaces of study area. Scanning time for everyscanning was determined. DEMs of the study area have beengenerated for every scanning interval; volume of the test areahas been calculated from certain height level and obtainedresults analyzed.

TOPCON 8203M Robotic Total Station GeodeticMeasurement InstrumentThe 8203M robotic total station instrument has a FC 100control unit. This instrument can measure up to 1200 m

TECHNIQUES by M. Yakar

M. Yakar ([email protected]) is in the engineering faculty in the Department ofGeodesy and Photogrammetry at the Selcuk University, Konya, Turkey

[Correction added after online publication July 21, 2008: The author’s name wasincorrectly listed as Y. Murat, when it should have been listed as M. Yakar.We apologize for the error.]

52 EXPERIMENTAL TECHNIQUES March/April 2009doi: 10.1111/j.1747-1567.2009.00375.x

© 2009, Society for Experimental Mechanics

reflectorless (Fig. 4). It is possible to scan at equal intervals inhorizontal and vertical directions. Scanning intervals can bedifferent in vertical and horizontal directions. Scanning areacan be chosen as square or rectangular (Figs. 1 and 2). Mea-surement precision of this instrument on nonprism is 6(3mm1 2ppmxD*) for fine mode and 6(10 mm 1 2ppmxD*) forcoarse mode (D, distance).

Experimental ApplicationAn artificial object made of gypsum was created to verify accu-racy of the method. A well-known surface of object was pro-duced using different molds, which simulated real mountains,hills, etc. Obtained molds were adhered to chipboard base.Volume of the model was measured with overflowed waterin the laboratory. The object has been painted to preventabsorption of water to measure volume accurately (Fig. 3).

Volume of the object was measured thrice and was found as279,60 dm3 with 2, 24 mL error. The model was placed as

a: Horizontal Scanning Interval b: Vertical Scanning Interval (a=b or a b)Scanned points (x,y,z)

b

a

Fig. 1: Rectangular or square scanning sample

a: Horizontal Scanning Interval b: Vertical Scanning Interval (a=b or a b)Scanned points (x,y,z) Irregular scanning area

a

b

Fig. 2: Irregular scanning area sample

Fig. 3: The artificial field model

Fig. 4: The model and TOPCON 8203M Robotic Total Stationand secondary unit

Table 1—Scanning intervals, volume, RMSE, andapproach ratio

SCANNINGINTERVAL (cm)

VOLUME(dm3)

RMSE(mm)

APROACHRATIO (%)

2 287,84 1,3 97,05

4 291,71 1,4 95,66

5 254,41 1,7 90,98

8 242,05 2,1 86,57

10 237,24 2,5 84,85

DIGITAL ELEVATION MODEL GENERATION

March/April 2009 EXPERIMENTAL TECHNIQUES 53

vertical to measure using robotic total station in the labora-tory and scanned with different intervals of 2, 4, 5, 8, and 10cm. Obtained measurement data were transferred to Surfer8.0 software. In terms of scanning intervals, the volumes ofthe model and its DEMs were obtained. The volume obtained,root mean square error (RMSE), and the approaching ratios tothe real volume are seen in Table 1. The graphical relationbetween the scanning interval, RMSE, and the approaching

ratios to the real volume are seen in Fig. 6. Digital elevationmodels belonging to artificial field model are seen in Fig. 5.

STUDY AREA AND SCANNING

The study area was in the Aksaray province in central Tur-key. Its adjacent provinces are Konya along the west andsouth, Nigde to the southeast, Nevsehir to the east, and

Fig. 5: Digital elevation models relating to the artificial test field according to scanning intervals


54 EXPERIMENTAL TECHNIQUES March/April 2009

Fig. 6: Relationship between scanning interval, RMSE, andvolume

Fig. 7: Location of study area

Table 2—Scanning intervals, time, measured pointsnumber, and RMSE

SCANNINGINTERVALS (cm)

SCANNINGTIME (min)

NUMBER OFMEASURED POINTS

20 393 2948

40 189 1475

80 96 745

100 68 591



Kırsehir to the north. This is a region of great natural beauty:Aksaray is one of the four provinces in the much visited areaof Cappadocia, along with Nevsehir, Nigde, and Kayseri(Fig. 7).

The study area had 29.8% slope, 12.12-m height difference,and 42.15 3 40.25-m dimensions (Fig. 7). A local geodetic netwas established. The study area was scanned by robotic totalstation geodetic measurement instrument in intervals of 20,40, 80, and 100 cm as equal in vertical and horizontal direc-tions. Times of the scanning for every scanning were recorded(Table 2). Two thousand nine hundred forty-eight points for

20-cm interval, 1477 points for 40-cm interval, 745 points for80-cm interval, and 591 points for 100-cm interval were mea-sured, and 3-D coordinates (xyz) of the study area wereacquired (Fig. 8). These coordinates were transferred toSurfer 8.0 software. Natural neighbor interpolation method,which is one of the most suitable methods, has been selected togenerate DEM of the study area (Fig. 9).17 Volume of thestudy area was calculated from the same height. RMSEs inscanning intervals were computed. The better RMSE wasobtained from 20-cm interval scanning (Table 3). Relationbetween scanning interval, RMSE, and volume is shown inFig. 10.

1915

1920

1925

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1280

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1295

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1315 20 cm scanning interval

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1915

1920

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1915

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Fig. 8: Point distribution according to scanning intervals



RMSE 5

ffiffiffiffiffiffiffiffiffiffiffi+V

2i

n21

s

Vi 5 Zm 2Zc

where Zm 5 measured, Zc 5 calculated, and n 5 number ofpoints.

RESULTS

Digital elevation models have been obtained with digitizing ofthe existing contour maps, field data, or using and evaluatingthe aerial photographs or satellite images of the field. The

production accuracy of the maps, digitizing accuracy, resolu-tion of the aerial or satellite images, and operator errors alleffect the quality of DEM.

Fig. 9: Generated DEMs according to scanning intervals

Table 3—Scanning intervals, RMSE, and volumes

SCANNING INTERVAL (cm) RMSE (cm) VOLUME (m3)

20 1,4 507 414, 48

40 1,5 505 021, 41

80 1,6 499 923, 59

100 1,8 495 598, 61



In this study, field data was used for generating DEM. Thefield data was obtained by robotic total station geodetic mea-surement instrument. DEMs, which have high accuracy, canbe generated (RMSE 5 1.5 cm). But, scanning time is veryhigh in this instrument. Study area (1697 m2) was scanned at6.55 h with intervals of 20 cm in vertical and horizontaldirections.

The most accurate volume on irregular surfaces is obtainedfrom the surface, which can be defined the most accurately.There is a close relation between the definition of the sur-face and the scanning interval. When Figs. 6 and 10 areexamined, as the scanning interval increases, RMSE alsoincreases (R2 5 0.9348 in artificial field model, R2 50.9657 in field). There is a linear relation between the scan-ning interval and the RMSE (R2 5 0.9348 in artificial fieldmodel). As the scanning interval increases, the real volumedecreases.

In robotic scanning, characteristic points of the field sur-face (i.e., acme point, talveg) cannot be measured connectedwith the scanning interval. This situation has negativeeffect on quality of DEM. This is a disadvantage for roboticscanning.

CONCLUSIONS

Digital elevation models are data files that contain the ele-vation of the terrain on a specified area, usually at a fixedgrid interval. The intervals between each of the grid pointswill always be referenced to xyz coordinate system. Thecloser the grid points are located, the more detailed theinformation will be included. Robotic total stations havebeen rapidly developed and used as tools for capturing 3-Dsurvey data in a variety of applications. The advantages ofrobotic total stations include automatic scanning, autono-mous operation, and accurate scanning. It is possible togenerate accurate DEMs of small areas by using this roboticmeasurement instrument. Digital elevation models areoften used to derive and monitor surfaces of inaccessibleand risky areas. This method is a noncontact method. So,robotic total station is very useful in such inaccessible andrisky areas. But some specific points such as the corner of

the scanning area may not be measured according to theselected scanning interval. Thus, obtained results maychange and will definitely affect the accuracy of the method.For this reason, the scanning area can be selected in theform of a rectangle. This remedy can increase the scanningtime. In spite of increasing time, the best solution of thisproblem is this approach.

The most important disadvantage of this instrument is thelong surveying time. It is seen that this instrument can beused in small areas and applications which do not require fastsurveying time such as mining, landslide, and geological stud-ies. Various engineering applications can also be completedusing robotic total stations.

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Silvera, N., and Phachomphon, K., ‘‘Accuracy of Interpolation Techni-ques for the Derivation of Digital Elevation Models in Relation to Land-form Types and Data Density,’’ Geomorphology 77: 126–132 (2006).

2. Rees, W.G., ‘‘The Accuracy of Digital Elevation Models Inter-polated to Higher Resolutions,’’ International Journal of RemoteSensing 21: 7–20 (2000).

3. Wise, S.M., ‘‘Assessing the Quality for Hydrological Applica-tions of Digital Elevation Models Derived from Contours,’’ Hydro-logical Processes 14: 1909–1929 (2000).

4. Ziadat, F.M., Taylor, J.C., and Brewer, T.R., ‘‘Merging Land-sat TM Imagery with Topographic Data to Aid Soil Mapping in theBadia Region of Jordan,’’ Journal of Arid Environment 54: 527–41(2003).

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8. Hutchinson, M.F., ‘‘Calculation of Hydrologically Sound Dig-ital Elevation Models,’’ Proceedings of the Third International Sym-posium on Spatial Data Handling, Sydney, Australia, pp. 117–133(1988).

9. Hutchinson, M.F., ‘‘A New Procedure for Gridding Elevationand Stream Line Data with Automatic Removal of Spurious Pits,’’Journal of Hydrology 106: 211–232 (1989).

10. Burrough, P.E., and McDonnell, R.A., ‘‘Principles of Geo-graphic Information Systems,’’ Oxford University Press, New York(1998).

11. Garbrecht, J., and Martz, L.W., ‘‘Digital Elevation ModelIssues in Water Resources Modeling,’’ Maidment, D.R. and Djokic,D., (eds), Hydrologic and Hydraulic Modeling Support withGeographical Information Systems, ESRI Press, Redlands, CA, pp.1–28 (2000).

12. Gao, J., ‘‘Comparison of Sampling Schemes in ConstructingDTMs from Topographic Maps,’’ ITC Journal 1:18–22 (1995).

13. Lynch, S., ‘‘Digital Elevation Models and Spatial Resolution,’’South African Journal of Science 98: 219–222 (2002).

14. Alparslan, E., and Aydoner, C., ‘‘Joint Analysis of Land UseCapability Class Data with Topography: the Kocaeli Province CaseStudy, 3rd GIS Days in Turkey, 6–9 October 2004,’’ Istanbul, URL

Fig. 10: Relationship between scanning interval, RMSE, andvolume



http://cbs2004.fatih.edu.tr/download/file477.pdf (2004) [accessed 1March 2008].

15. Tchoukanski, I., ‘‘Triangulated Irregular Network (TIN) Notes,’’URL http://www.ian-ko.com/resources/triangulated_irregular_network.htm, (2008) [accessed 1 March 2008].

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17. Yilmaz, H.M., ‘‘The Effect of Interpolation Methods in Sur-face Definition: an Experimental Study,’’ Earth Surface Processesand Landforms 32(9): 1346–1361 (2007). n



digital elevation model generation by robotic total station instrument

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